Learning to Optimize Energy Efficiency in Energy Harvesting Wireless Sensor Networks

We study wireless power transmission by an energy source to multiple energy harvesting nodes with the aim to maximize the energy efficiency. The source transmits energy to the nodes using one of the available power levels in each time slot and the nodes transmit information back to the energy source...

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Bibliographic Details
Published in:IEEE wireless communications letters 2021-06, Vol.10 (6), p.1153-1157
Main Authors: Ghosh, Debamita, Hanawal, Manjesh K., Zlatanov, Nikola
Format: Article
Language:English
Subjects:
Algorithms
Batteries
Energy efficiency
Energy harvesting
Energy sources
Indexes
Information processing
Multi-armed bandit problems
Multi-armed bandits
Nodes
Optimization
Receivers
Transmitters
Wireless networks
Wireless power transmission
Wireless sensor networks
wirelessly powered communication network
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Summary:We study wireless power transmission by an energy source to multiple energy harvesting nodes with the aim to maximize the energy efficiency. The source transmits energy to the nodes using one of the available power levels in each time slot and the nodes transmit information back to the energy source using the harvested energy. The source does not have any channel state information and it only knows whether a received codeword from a given node was successfully decoded or not. With this limited information, the source has to learn the optimal power level that maximizes the energy efficiency of the network. We model the problem as a stochastic Multi-Armed Bandits problem and develop an Upper Confidence Bound based algorithm, which learns the optimal transmit power of the energy source that maximizes the energy efficiency. Numerical results validate the performance guarantees of the proposed algorithm and show significant gains compared to the benchmark schemes.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2021.3058170